Clustering Student Competencies Using the K-Means Algorithm

  This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas...

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Bibliographic Details
Published inUltimatics : Jurnal Teknik Informatika Vol. 17; no. 1; pp. 99 - 106
Main Authors Andini, Ratih Friska Dwi, Liantoni, Febri, Budianto, Aris
Format Journal Article
LanguageEnglish
Published 01.07.2025
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Summary:  This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which falls into the medium category. This study concludes that the use of the K-Means algorithm alone is sufficient to support the analysis of student areas of competence, with potential applications as a recommendation system for students in choosing elective courses and as an evaluation tool for study programs to identify areas of competence that need improvement.
ISSN:2085-4552
2581-186X
DOI:10.31937/ti.v17i1.4071